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Open AccessJournal ArticleDOI

RNA-Seq: a revolutionary tool for transcriptomics

Zhong Wang, +2 more
- 01 Jan 2009 - 
- Vol. 10, Iss: 1, pp 57-63
TLDR
The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Abstract
RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

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Citations
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Journal ArticleDOI

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Journal ArticleDOI

Sequencing technologies-the next generation

TL;DR: A technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments is presented.
Journal ArticleDOI

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Journal ArticleDOI

A scaling normalization method for differential expression analysis of RNA-seq data

TL;DR: A simple and effective method for performing normalization is outlined and dramatically improved results for inferring differential expression in simulated and publicly available data sets are shown.
Journal ArticleDOI

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
References
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Journal ArticleDOI

The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

TL;DR: A quantitative sequencing-based method is developed for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome, and it is demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed.
Journal ArticleDOI

Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis

TL;DR: Deep sequencing of smRNAs revealed a direct relationship between the location of sm RNAs and DNA methylation, perturbation of smRNA biogenesis upon loss of CpG DNA methylisation, and a tendency for smRN as to direct strand-specific DNA methylations in regions of RNA-DNA homology.
Journal Article

Serial analysis of gene expression

TL;DR: Serial analysis of gene expression (SAGE) is a sequenced-based technique, which permits comprehensive and quantitative gene expression profiles from specific tissues or cells, which has been successfully applied for transcriptome research and identification of differentially expressed genes between mRNA populations.
Journal ArticleDOI

Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays

TL;DR: A novel sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads provides an unprecedented depth of analysis permitting application of powerful statistical techniques for discovery of functional relationships among genes.
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